IEEE Trans Neural Netw Learn Syst - Retargeted Least Squares Regression Algorithm.

Tópicos

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{ model(2341) predict(2261) use(1141) }
{ featur(3375) classif(2383) classifi(1994) }
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{ imag(1057) registr(996) error(939) }
{ framework(1458) process(801) describ(734) }
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Resumo

This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero-one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with the traditional least squares regression (LSR) and a recently proposed discriminative LSR models, ReLSR is much more accurate in measuring the classification error of the regression model. Furthermore, ReLSR is a single and compact model, hence there is no need to train two-class (binary) machines that are independent of each other. The convex optimization problem of ReLSR is solved elegantly and efficiently with an alternating procedure including regression and retargeting as substeps. The experimental evaluation over a range of databases identifies the validity of our method.

Resumo Limpo

brief present framework retarget least squar regress relsr multicategori classif core idea direct learn regress target data use tradit zeroon matrix regress target learn target matrix can guarante larg margin constraint requir correct classif data point compar tradit least squar regress lsr recent propos discrimin lsr model relsr much accur measur classif error regress model furthermor relsr singl compact model henc need train twoclass binari machin independ convex optim problem relsr solv eleg effici altern procedur includ regress retarget substep experiment evalu rang databas identifi valid method

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